Lights, camera, action!

Today, we’re going to take a deep dive into the world of HBO movies and TV shows. From the iconic dramas like The Sopranos and Game of Thrones to the latest releases. HBO has been providing quality content to its viewers for decades, but have you ever wondered how they make decisions about what shows to produce or which movies to acquire? That’s where the fascinating world of HBO data comes into play. By analyzing audience trends, ratings, and viewer demographics, HBO can make informed decisions about what to offer to its loyal fans. So sit back, grab a snack, and get ready to explore the exciting world of HBO data.

Installing packages

#install.packages("magick")
#install.packages("kableExtra")
#install.packages("countrycode")


library(countrycode)
library(tidyverse)
library(knitr)
library(kableExtra)
library(maps)

#remember to put how each package is used

About Our Data

The data we’ve decided to work on is from kaggle and is owned by Diego Enrique and here’s the link https://www.kaggle.com/datasets/dgoenrique/hbo-max-movies-and-tv-shows

Let us read our datas, shall we?

We’re using the kable and head function to show a part of the data sets we’re working on but in an organized manner

Here’s our credits.csv

And here’s our titles.csv

Firstly,let’s see how many movies and TV shows we are dealing with

##    type    n
## 1 MOVIE 2408
## 2  SHOW  622

Wow! that’s a lot more movies than shows! But let’s see it in a graph

And what’s the distribution of genres do we have from both?

Lets see it in a bar graph

Here’s the table of number of genres in descending order

kable(genre_counts, caption = "Number of movies and TV shows by genre")

Since we just finished oberserving the number of genres in our dataset

Let us see if there’s a correlation between age_restriction and genres

## removing missing values
titles = na.omit(titles)


## Cleaning our age certification column 
#titles$age_certification = gsub( " .*", "", titles$age_certification)

age_genre = genre_counts %>% 
  group_by(age_certification,genres) %>% 
  summarize(age_genre_count = n()) %>% 
  ungroup ()

## Plotting
ggplot(age_genre,aes(x=age_certification , y=age_genre_count, fill = genres)) + geom_bar(stat = "identity", position = "dodge") +
  labs(x = "Age Certification", y = "Number of Titles", title = "Age Certification and Genres") +
  theme_minimal() +
  theme(legend.position = "bottom") +
  guides(fill = guide_legend(nrow = 2, byrow = TRUE))

unique(titles$age_certification)

## [1] ""         "TV-G"     "TV-Y"     "TV-Y7"    "TV-PG"    "TV-14"    "TV-MA"   
## [8] "TV-Y7-FV"

Here are the number of shows available in HBO as a function of time

Now let’s see what are the top 10 most popular movies and show from imbd and tmdb

Here are HBO’s top 10 movies

Ane here HBO’s top 10 shows

top_10_shows <- titles %>% 
  filter(type == "SHOW") %>%
  arrange(desc(imdb_score)) %>%
  select(title, type, release_year, genres, ) %>%
  head(10)

  kable(head(top_10_shows),
      align = "c",
      caption = "HBO's top 10 shows",
      format = "html")

You can see there is a wide range of movies and tv shows, especially what year they were released. I wonder what the newest and oldest movies are?

## [1] title        type         release_year genres      
## <0 rows> (or 0-length row.names)

## [1] title        type         release_year genres      
## <0 rows> (or 0-length row.names)

##          title type release_year                                        genres
## 1 Looney Tunes SHOW         1929 ['comedy', 'family', 'thriller', 'animation']

##            title type release_year
## 1 The Last of Us SHOW         2023
##                                               genres
## 1 ['drama', 'action', 'horror', 'scifi', 'thriller']

I definitely have not seen either of those movies, but everyone should know last of us because of tiktok.

Now I am wondering what is the longest movie?

shortest_movie <- titles %>%
  filter(type == "MOVIE") %>%
  arrange(runtime) %>%
  select(title, type,runtime, release_year, genres) %>%
  head(1)

shortest_movie

## [1] title        type         runtime      release_year genres      
## <0 rows> (or 0-length row.names)

longest_movie <- titles %>%
  filter(type == "MOVIE") %>%
  arrange(desc(runtime)) %>%
  select(title, type, runtime, release_year, genres) %>%
  head(1)

longest_movie

## [1] title        type         runtime      release_year genres      
## <0 rows> (or 0-length row.names)

shortest_show <- titles %>%
  filter(type == "SHOW") %>%
  arrange(runtime, seasons) %>%
  select(title, type, runtime, seasons, release_year, genres) %>%
  head(1)

shortest_show

##                 title type runtime seasons release_year     genres
## 1 Garfunkel and Oates SHOW       4       1         2012 ['comedy']

longest_show <- titles %>%
  filter(type == "SHOW") %>%
  arrange(desc(runtime)) %>%
  arrange(desc(seasons)) %>%
  select(title, type, runtime, seasons, release_year, genres) %>%
  head(1)

longest_show

##           title type runtime seasons release_year
## 1 Sesame Street SHOW      51      53         1969
##                                                  genres
## 1 ['comedy', 'animation', 'family', 'fantasy', 'music']

Now lets look at the credits data.

credits %>%
  count(role)

##       role     n
## 1    ACTOR 62158
## 2 DIRECTOR  2721

Are any of these actors/directors in multiple projects? If so, who was in the most projects?

project_count <- credits %>%
  count(name)

glimpse(project_count)

## Rows: 45,276
## Columns: 2
## $ name <chr> "'Auntie' Mackay", "'Little Man' Machan", "'Weird Al' Yankovic", …
## $ n    <int> 1, 3, 3, 1, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1,…

most_projects <- credits %>% 
  count(name) %>% 
  slice_max(n)

most_projects

##           name  n
## 1 Grey DeLisle 60

##Who is this person?

credits %>% 
  filter(name == "Grey DeLisle")

##    person_id        id         name
## 1      14142   ts21507 Grey DeLisle
## 2      14142   ts21601 Grey DeLisle
## 3      14142   ts20381 Grey DeLisle
## 4      14142   ts22480 Grey DeLisle
## 5      14142    ts5042 Grey DeLisle
## 6      14142   tm94209 Grey DeLisle
## 7      14142   tm93784 Grey DeLisle
## 8      14142   tm58727 Grey DeLisle
## 9      14142  tm656353 Grey DeLisle
## 10     14142   tm43061 Grey DeLisle
## 11     14142   tm23583 Grey DeLisle
## 12     14142  tm140798 Grey DeLisle
## 13     14142   ts20574 Grey DeLisle
## 14     14142  tm167763 Grey DeLisle
## 15     14142   tm62126 Grey DeLisle
## 16     14142   tm30342 Grey DeLisle
## 17     14142  tm167388 Grey DeLisle
## 18     14142   tm65596 Grey DeLisle
## 19     14142  tm160240 Grey DeLisle
## 20     14142  tm160231 Grey DeLisle
## 21     14142  tm151689 Grey DeLisle
## 22     14142  tm179476 Grey DeLisle
## 23     14142  tm177932 Grey DeLisle
## 24     14142    ts3139 Grey DeLisle
## 25     14142  tm159051 Grey DeLisle
## 26     14142  tm171655 Grey DeLisle
## 27     14142   tm63619 Grey DeLisle
## 28     14142  tm152510 Grey DeLisle
## 29     14142  tm152501 Grey DeLisle
## 30     14142   ts37956 Grey DeLisle
## 31     14142  tm195247 Grey DeLisle
## 32     14142  tm244555 Grey DeLisle
## 33     14142  tm238389 Grey DeLisle
## 34     14142  tm193859 Grey DeLisle
## 35     14142  tm244564 Grey DeLisle
## 36     14142  tm219341 Grey DeLisle
## 37     14142  tm214711 Grey DeLisle
## 38     14142  tm244119 Grey DeLisle
## 39     14142  tm244479 Grey DeLisle
## 40     14142  tm138025 Grey DeLisle
## 41     14142  tm365731 Grey DeLisle
## 42     14142  tm422417 Grey DeLisle
## 43     14142  tm301058 Grey DeLisle
## 44     14142  tm372838 Grey DeLisle
## 45     14142  tm361837 Grey DeLisle
## 46     14142  tm326858 Grey DeLisle
## 47     14142  tm414009 Grey DeLisle
## 48     14142  tm405461 Grey DeLisle
## 49     14142  tm317933 Grey DeLisle
## 50     14142  tm423754 Grey DeLisle
## 51     14142  tm820756 Grey DeLisle
## 52     14142   ts89867 Grey DeLisle
## 53     14142  tm894108 Grey DeLisle
## 54     14142  tm883958 Grey DeLisle
## 55     14142 tm1248448 Grey DeLisle
## 56     14142 tm1028015 Grey DeLisle
## 57     14142 tm1065433 Grey DeLisle
## 58     14142  tm930306 Grey DeLisle
## 59     14142 tm1171238 Grey DeLisle
## 60     14142  tm987899 Grey DeLisle
##                                                                           character
## 1                                                              Daphne Blake (voice)
## 2                                                        The High Priestess (voice)
## 3                                                              Daphne Blake (voice)
## 4                                                                     Mandy (voice)
## 5                                                  Frances 'Frankie' Foster (voice)
## 6                                                                    Daphne (voice)
## 7                                                              Daphne Blake (voice)
## 8                                             Daphne / Cat Witch / Honeybee (voice)
## 9  Frankie Foster / Tiny Friend / Little Boy Voice / Lady (voice) (as Grey DeLisle)
## 10                                        Crazy Old Cat Lady/Gramma Stuffum (voice)
## 11                                                                   Daphne (voice)
## 12                                                                   Daphne (voice)
## 13                                                                                 
## 14                                                           Barbara Gordon (voice)
## 15                                             Anchor Carla / Female Mutant (voice)
## 16                                                        Lois Lane / Queen (voice)
## 17                                       Ree'Yu / Ardakian Trawl / Boodikka (voice)
## 18                                                         Young Manchester (voice)
## 19                                                             Daphne Blake (voice)
## 20                                                             Daphne Blake (voice)
## 21                                            Grandmother (voice) (as Grey Griffin)
## 22                    Nora Allen / Young Barry Allen / Martha Wayne / Joker (voice)
## 23                                                             Anchor Carla (voice)
## 24                                                Margaret Sorrow / Magpie  (voice)
## 25                       Wonder Woman / Superbaby (voice) (as Grey DeLisle Griffin)
## 26                                                             Daphne Blake (voice)
## 27                                                             Daphne Blake (voice)
## 28                                                             Daphne Blake (voice)
## 29                                                             Daphne Blake (voice)
## 30                                                             Daphne Blake (voice)
## 31                                                          Tina / Platinum (voice)
## 32                                                             Daphne Blake (voice)
## 33                                                             Wonder Woman (voice)
## 34                                                                 Samantha (voice)
## 35                                                             Wonder Woman (voice)
## 36                                                 Wonder Woman / Lois Lane (voice)
## 37                                                             Daphne Blake (voice)
## 38                                                             Daphne Blake (voice)
## 39                                                             Wonder Woman (voice)
## 40                                                             Daphne Blake (voice)
## 41                                Sister Leslie / Jason / Additional Voices (voice)
## 42                                                             Daphne Blake (voice)
## 43                                                             Daphne Blake (voice)
## 44                        Wonder Woman / Diana Prince (voice) and Lois Lane (voice)
## 45                                              Daphne Blake / Black Canary (voice)
## 46                                                             Daphne Blake (voice)
## 47           Diana Prince / Wonder Woman (voice) / Lois Lane (voice) / Ring (voice)
## 48                                                         Wonder Woman / Lois Lane
## 49                                                  Wonder Woman / Platinum (voice)
## 50                                                             Wonder Woman (voice)
## 51                                                               Mrs. Claus (Voice)
## 52                                                             Daphne Blake (voice)
## 53                                                        Additional Voices (voice)
## 54                                         Wonder Woman (voice) / Lois Lane (voice)
## 55                                     Daphne / Daisy / Musketeer 1 / Olive (voice)
## 56                                   Beelzebub / Little Della / Little Jack (voice)
## 57                                         Daphne Blake / Frau Glockenspiel (voice)
## 58                                                                 Lady Eve (voice)
## 59                                              Diana Prince / Wonder Woman (voice)
## 60                                                             Daphne Blake (voice)
##     role
## 1  ACTOR
## 2  ACTOR
## 3  ACTOR
## 4  ACTOR
## 5  ACTOR
## 6  ACTOR
## 7  ACTOR
## 8  ACTOR
## 9  ACTOR
## 10 ACTOR
## 11 ACTOR
## 12 ACTOR
## 13 ACTOR
## 14 ACTOR
## 15 ACTOR
## 16 ACTOR
## 17 ACTOR
## 18 ACTOR
## 19 ACTOR
## 20 ACTOR
## 21 ACTOR
## 22 ACTOR
## 23 ACTOR
## 24 ACTOR
## 25 ACTOR
## 26 ACTOR
## 27 ACTOR
## 28 ACTOR
## 29 ACTOR
## 30 ACTOR
## 31 ACTOR
## 32 ACTOR
## 33 ACTOR
## 34 ACTOR
## 35 ACTOR
## 36 ACTOR
## 37 ACTOR
## 38 ACTOR
## 39 ACTOR
## 40 ACTOR
## 41 ACTOR
## 42 ACTOR
## 43 ACTOR
## 44 ACTOR
## 45 ACTOR
## 46 ACTOR
## 47 ACTOR
## 48 ACTOR
## 49 ACTOR
## 50 ACTOR
## 51 ACTOR
## 52 ACTOR
## 53 ACTOR
## 54 ACTOR
## 55 ACTOR
## 56 ACTOR
## 57 ACTOR
## 58 ACTOR
## 59 ACTOR
## 60 ACTOR

Ever wonder if your most favourite genre happens to be the most popular genre on HBO?

Let’s find out

Here’s both those graphs combined

genre_popularity <- titles %>%
  mutate(genres = str_remove_all(genres, "'")) %>% 
  mutate(genres = gsub("\\[", "", genres)) %>% 
  mutate(genres = gsub("\\]", "", genres)) %>% 
  separate_rows(genres, sep = ", ") %>%
  group_by(genres, type,tmdb_popularity,tmdb_score, imdb_score ) %>%
  summarize(Count = n()) %>%
  ungroup() %>%
  arrange(desc(tmdb_popularity))

genre_popularity

## # A tibble: 1,523 × 6
##    genres   type  tmdb_popularity tmdb_score imdb_score Count
##    <chr>    <chr>           <dbl>      <dbl>      <dbl> <int>
##  1 action   SHOW            3481.       8.80        9.1     1
##  2 drama    SHOW            3481.       8.80        9.1     1
##  3 horror   SHOW            3481.       8.80        9.1     1
##  4 scifi    SHOW            3481.       8.80        9.1     1
##  5 thriller SHOW            3481.       8.80        9.1     1
##  6 action   SHOW             559.       8.4         9.2     1
##  7 drama    SHOW             559.       8.4         9.2     1
##  8 fantasy  SHOW             559.       8.4         9.2     1
##  9 romance  SHOW             559.       8.4         9.2     1
## 10 scifi    SHOW             559.       8.4         9.2     1
## # … with 1,513 more rows

# Create the bar chart
ggplot(genre_popularity, aes(x = reorder(genres, Count), y = tmdb_popularity, fill = type)) +
  geom_bar(stat = "identity")  +
  labs(x = "Genre", y = "tmdb_popularity", title = "Genres and its popularity") +
  theme_light()+coord_flip()

Who would’ve know?!

Let us look at the Number of movies and TV shows by country

Unfortunately, because HBO only got their movies and shows from 99 countries, there are some uncolored countries